Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
UFLOW: visualizing uncertainty in fluid flow
Proceedings of the 7th conference on Visualization '96
Hierarchical parallel coordinates for exploration of large datasets
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Uncertainty-Based Information: Elements of Generalized Information Theory
Uncertainty-Based Information: Elements of Generalized Information Theory
Glyphs for Visualizing Uncertainty in Vector Fields
IEEE Transactions on Visualization and Computer Graphics
A Taxonomy of Visualization Techniques Using the Data State Reference Model
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
A Next Step: Visualizing Errors and Uncertainty
IEEE Computer Graphics and Applications
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Information Visualization: Perception for Design
Information Visualization: Perception for Design
Parallel Coordinates for Exploring Properties of Subsets
CMV '04 Proceedings of the Second International Conference on Coordinated & Multiple Views in Exploratory Visualization
Uncovering Clusters in Crowded Parallel Coordinates Visualizations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Rethinking Visualization: A High-Level Taxonomy
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
The sampling lens: making sense of saturated visualisations
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Low-Level Components of Analytic Activity in Information Visualization
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization
Outlier-Preserving Focus+Context Visualization in Parallel Coordinates
IEEE Transactions on Visualization and Computer Graphics
Measuring Data Abstraction Quality in Multiresolution Visualizations
IEEE Transactions on Visualization and Computer Graphics
Enabling Automatic Clutter Reduction in Parallel Coordinate Plots
IEEE Transactions on Visualization and Computer Graphics
Theoretical Foundations of Information Visualization
Information Visualization
Revealing uncertainty for information visualization
Information Visualization
Pargnostics: Screen-Space Metrics for Parallel Coordinates
IEEE Transactions on Visualization and Computer Graphics
An Information-theoretic Framework for Visualization
IEEE Transactions on Visualization and Computer Graphics
Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization
IEEE Transactions on Visualization and Computer Graphics
Adaptive Privacy-Preserving Visualization Using Parallel Coordinates
IEEE Transactions on Visualization and Computer Graphics
The importance of tracing data through the visualization pipeline
Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization
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Uncertainty is an intrinsic part of any visual representation in visualization, no matter how precise the input data. Existing research on uncertainty in visualization mainly focuses on depicting data-space uncertainty in a visual form. Uncertainty is thus often seen as a problem to deal with, in the data, and something to be avoided if possible. In this paper, we highlight the need for analyzing visual uncertainty in order to design more effective visual representations. We study various forms of uncertainty in the visual representation of parallel coordinates and propose a taxonomy for categorizing them. By building a taxonomy, we aim to identify different sources of uncertainty in the screen space and relate them to different effects of uncertainty upon the user. We examine the literature on parallel coordinates and apply our taxonomy to categorize various techniques for reducing uncertainty. In addition, we consider uncertainty from a different perspective by identifying cases where increasing certain forms of uncertainty may even be useful, with respect to task, data type and analysis scenario. This work suggests that uncertainty is a feature that can be both useful and problematic in visualization, and it is beneficial to augment an information visualization pipeline with a facility for visual uncertainty analysis. © 2012 Wiley Periodicals, Inc.